
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


# Load your data and print out a few lines. Perform operations to inspect data
#   types and look for instances of missing or possibly errant data.
# Read the CSV File Using Pandas read_csv function
reader = pd.read_csv('tmdb-movies.csv')   

# print the concise summery of the dataset
reader.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10866 entries, 0 to 10865
Data columns (total 21 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   id                    10866 non-null  int64  
 1   imdb_id               10856 non-null  object 
 2   popularity            10866 non-null  float64
 3   budget                10866 non-null  int64  
 4   revenue               10866 non-null  int64  
 5   original_title        10866 non-null  object 
 6   cast                  10790 non-null  object 
 7   homepage              2936 non-null   object 
 8   director              10822 non-null  object 
 9   tagline               8042 non-null   object 
 10  keywords              9373 non-null   object 
 11  overview              10862 non-null  object 
 12  runtime               10866 non-null  int64  
 13  genres                10843 non-null  object 
 14  production_companies  9836 non-null   object 
 15  release_date          10866 non-null  object 
 16  vote_count            10866 non-null  int64  
 17  vote_average          10866 non-null  float64
 18  release_year          10866 non-null  int64  
 19  budget_adj            10866 non-null  float64
 20  revenue_adj           10866 non-null  float64
dtypes: float64(4), int64(6), object(11)
memory usage: 1.7+ MB


#print first two rows of the dataset
reader.head(2)


#print Last two rows of the dataset
reader.tail(2)


#print concise summery of the dataset
reader.describe()


#since the dataset contain null values also 
#count total rows in each column which contain null values
reader.isna().sum()

id                         0
imdb_id                   10
popularity                 0
budget                     0
revenue                    0
original_title             0
cast                      76
homepage                7930
director                  44
tagline                 2824
keywords                1493
overview                   4
runtime                    0
genres                    23
production_companies    1030
release_date               0
vote_count                 0
vote_average               0
release_year               0
budget_adj                 0
revenue_adj                0
dtype: int64


#fill the null values with zero using 'fillna' function
#I don't want to update it with zero because some rows also contain string values.
reader.fillna(0)
print()


#'duplicated()' function in pandas return the duplicate row as True and othter as False
#for counting the duplicate elements we sum all the rows
sum(reader.duplicated())

1


#After calculating the duplicate row we can drop these row using 'drop_duplicates()' function
reader.drop_duplicates(inplace = True)
#afetr removing duplicate value from the dataset
print("Afetr Removing Duplicate Values (Rows,Columns) : ",reader.shape)

Afetr Removing Duplicate Values (Rows,Columns) :  (10865, 21)


#the the given in the dataset is in string format.
#So we need to change this in datetime format

reader['release_date'] = pd.to_datetime(reader['release_date'])
reader['release_date'].head()

0   2015-06-09
1   2015-05-13
2   2015-03-18
3   2015-12-15
4   2015-04-01
Name: release_date, dtype: datetime64[ns]


# After discussing the structure of the data and any problems that need to be
#   cleaned, perform those cleaning steps in the second part of this section
#we can see that 21 columns in the dataset, We can drop them using drop function.
reader.drop(['budget_adj','revenue_adj','overview','imdb_id','homepage','tagline'],axis =1,inplace = True)
print("Afetr Removing Unused Columns (Rows,Columns) : ",reader.shape)

Afetr Removing Unused Columns (Rows,Columns) :  (10865, 15)


# Checking for an zero values in the budget and revenue columns
print("Rows With Zero Values In The Budget Column:",reader[(reader['budget']==0)].shape[0])
print("Rows With Zero Values In The Revenue Column:",reader[(reader['revenue']==0)].shape[0])

Rows With Zero Values In The Budget Column: 5696
Rows With Zero Values In The Revenue Column: 6016


# make group for each year and count the number of movies in each year 
data=reader.groupby('release_year').count()['id']
print(data.tail())

#make group of the data according to their release year and count the total number of movies in each year and pot.
reader.groupby('release_year').count()['id'].plot(xticks = np.arange(1960,2016,5))

#set the figure size and labels
sns.set(rc={'figure.figsize':(10,5)})
plt.title("Year Vs Number Of Movies",fontsize = 14)
plt.xlabel('Release year',fontsize = 13)
plt.ylabel('Number Of Movies',fontsize = 13)
#set the style sheet
sns.set_style("whitegrid")

release_year
2011    540
2012    588
2013    659
2014    700
2015    629
Name: id, dtype: int64


#calculate Profit for each of the movie
#add a new column in the dataframe name 'Profit'
reader['Profit'] = reader['revenue'] - reader['budget']


#use the function 'idmin' to find the index of lowest profit movie.
#use the function 'idmax' to find the index of Highest profit movie.
#print the row related to this index.
def find_minmax(x):
    #use the function 'idmin' to find the index of lowest profit movie.
    min_index = reader[x].idxmin()
    #use the function 'idmax' to find the index of Highest profit movie.
    high_index = reader[x].idxmax()
    high = pd.DataFrame(reader.loc[high_index,:])
    low = pd.DataFrame(reader.loc[min_index,:])
    
    #print the movie with high and low profit
    print("Movie Which Has Highest "+ x + " : ",reader['original_title'][high_index])
    print("Movie Which Has Lowest "+ x + "  : ",reader['original_title'][min_index])
    return pd.concat([high,low],axis = 1)

#call the find_minmax function.
find_minmax('Profit')

Movie Which Has Highest Profit :  Avatar
Movie Which Has Lowest Profit  :  The Warrior's Way


#make a plot which contain top 10 movies which earn highest profit.
#sort the 'Profit' column in decending order and store it in the new dataframe,
info = pd.DataFrame(reader['Profit'].sort_values(ascending = False))
info['original_title'] = reader['original_title']
data = list(map(str,(info['original_title'])))
x = list(data[:10])
y = list(info['Profit'][:10])

#make a plot usinf pointplot for top 10 profitable movies.
ax = sns.pointplot(x=y,y=x)

#setup the figure size
sns.set(rc={'figure.figsize':(10,5)})
#setup the title and labels of the plot.
ax.set_title("Top 10 Profitable Movies",fontsize = 15)
ax.set_xlabel("Profit",fontsize = 13)
sns.set_style("darkgrid")


#find the movie with highest and lowest budget.
#use 'find_minmax' function which i made earlier.
#make sure that movie with budget zero didn't affect the result.
#so change the zero into NAN in budget column.
reader['budget'] = reader['budget'].replace(0,np.NAN)
find_minmax('budget')

Movie Which Has Highest budget :  The Warrior's Way
Movie Which Has Lowest budget  :  Fear Clinic


#make a plot which contain top 10 highest budget movies.
#sort the 'budget' column in decending order and store it in the new dataframe.
info = pd.DataFrame(reader['budget'].sort_values(ascending = False))
info['original_title'] = reader['original_title']
data = list(map(str,(info['original_title'])))

#extract the top 10 budget movies data from the list and dataframe.
x = list(data[:10])
y = list(info['budget'][:10])

#plot the figure and setup the title and labels.
ax = sns.pointplot(x=y,y=x)
sns.set(rc={'figure.figsize':(10,5)})
ax.set_title("Top 10 High Budget Movies",fontsize = 15)
ax.set_xlabel("Budget",fontsize = 13)
sns.set_style("darkgrid")


#find the movie with highest and lowest earned revenue
#use 'find_minmax' function which i made earlier
find_minmax('revenue')

Movie Which Has Highest revenue :  Avatar
Movie Which Has Lowest revenue  :  Wild Card


#top 10 movies which made highest revenue.
#sort the 'revenue' column in decending order and store it in the new dataframe.
info = pd.DataFrame(reader['revenue'].sort_values(ascending = False))
info['original_title'] = reader['original_title']
data = list(map(str,(info['original_title'])))

#extract the top 10 movies with high revenue data from the list and dataframe.
x = list(data[:10])
y = list(info['revenue'][:10])

#make the point plot and setup the title and labels.
ax = sns.pointplot(x=y,y=x)
sns.set(rc={'figure.figsize':(10,5)})
ax.set_title("Top 10 High Revenue Movies",fontsize = 15)
ax.set_xlabel("Revenue",fontsize = 13)
sns.set_style("darkgrid")


Movie Which Has Highest revenue :  Avatar<br>
Movie Which Has Highest revenue :  Wild Card<span>(we extract it from table above the Graph)</span><br>
this Graph Show The Top High Revenue Movies


#find the movie with longest and shortest runtime.
#use 'find_minmax' function which i made earlier by passsing 'runtime' as argument.
#it can't be possible a movie with zero runtime,but dataset contain some data like this.
#so update zero with NAN in runtime column.
reader['runtime'] = reader['runtime'].replace(0,np.NAN)
find_minmax('runtime')

Movie Which Has Highest runtime :  The Story of Film: An Odyssey
Movie Which Has Lowest runtime  :  Fresh Guacamole


#top 10 Movies With Longest runtime
#sort the 'runtime' column in decending order and store it in the new dataframe.
info = pd.DataFrame(reader['runtime'].sort_values(ascending = False))
info['original_title'] = reader['original_title']
data = list(map(str,(info['original_title'])))

#extract the top 10 longest duraton movies data from the list and dataframe.
x = list(data[:10])
y = list(info['runtime'][:10])

#make the point plot and setup the title and labels.
ax = sns.pointplot(x=y,y=x)
sns.set(rc={'figure.figsize':(10,5)})
ax.set_title("Top 10 Longest Movies",fontsize = 15)
ax.set_xlabel("Runtime",fontsize = 13)
sns.set_style("darkgrid")


#find the movie with most and least votes.
#use 'find_minmax' function which i made earlier by passsing 'Vote_average' as argument.
find_minmax('vote_average')

Movie Which Has Highest vote_average :  The Story of Film: An Odyssey
Movie Which Has Lowest vote_average  :  Transmorphers


#top 10 highets rated movies.
#sort the 'vote_average' column in decending order and store it in the new dataframe.
info = pd.DataFrame(reader['vote_average'].sort_values(ascending = False))
info['original_title'] = reader['original_title']
data = list(map(str,(info['original_title'])))

##extract the top 10 highly rated movies data from the list and dataframe.
x = list(data[:10])
y = list(info['vote_average'][:10])

#make the point plot and setup the title and labels.
ax = sns.pointplot(x=y,y=x)
sns.set(rc={'figure.figsize':(10,5)})
ax.set_title("Top 10 Highest Rated Movies",fontsize = 15)
ax.set_xlabel("Vote Average",fontsize = 13)
#setup the stylesheet
sns.set_style("darkgrid")


#do year-wise analysis of profit earn by movies in each earn.
#take the average of profit made by movies in each year and plot.
#make the group of the data according to their release_year and find the mean profit and plot.
reader.groupby('release_year')['Profit'].mean().plot()

#setup the title and labels of the figure.
plt.title("Year Vs Average Profit",fontsize = 14)
plt.xlabel('Release year',fontsize = 13)
plt.ylabel('Average Profit',fontsize = 13)

#setup the figure size.
sns.set(rc={'figure.figsize':(10,5)})
sns.set_style("whitegrid")


#use groupby function and group the data according to their runtime.
#make a plot using their popularity and find which length movies are most popular.

#make the group of the data according to their runtime and find the mean popularity related to this and plot.
reader.groupby('runtime')['popularity'].mean().plot(figsize = (13,5),xticks=np.arange(0,1000,100))

#setup the title of the figure
plt.title("Runtime Vs Popularity",fontsize = 14)

#setup the x-label and y-label of the plot.
plt.xlabel('Runtime',fontsize = 13)
plt.ylabel('Average Popularity',fontsize = 13)

#setup the figure size.
sns.set(rc={'figure.figsize':(10,5)})
sns.set_style("whitegrid")


#how the runtime of the movies differ year to year.

#make the group of the data according to their release_year and find the mean  related to this and plot.
reader.groupby('release_year').mean()['runtime'].plot(xticks = np.arange(1960,2016,5))

#setup the figure size.
sns.set(rc={'figure.figsize':(10,5)})

#setup the title of the figure
plt.title("Runtime Vs Year",fontsize = 14)

#setup the x-label and y-label of the plot.
plt.xlabel('Year',fontsize = 13)
plt.ylabel('Runtime',fontsize = 13)
sns.set_style("whitegrid")


#how does revenue change according to their budget.
#make a scatter plot using 'regplot' between ''revenue' and 'budget'.
ax = sns.regplot(x=reader['revenue'], y=reader['budget'],color='c')

#set the title and labels of the figure
ax.set_title("Revenue Vs Budget",fontsize=13)
ax.set_xlabel("Revenue",fontsize=12)
ax.set_ylabel("Budget",fontsize=12)
#set the figure size
sns.set(rc={'figure.figsize':(6,4)})
sns.set_style("whitegrid")

#find the correlation between them
#change zero into NAN in budget and revenue column for the exact calculation.
reader['budget'] = reader['budget'].replace(0,np.NAN)
reader['revenue'] = reader['revenue'].replace(0,np.NAN)
reader['Profit'] = reader['Profit'].replace(0,np.NAN)

#find the correlation using 'corr()' function.
#it returns a dataframe which contain the correlation between all the numeric columns.
data_corr = reader.corr()
print("Correlation Between Revenue And Budget : ",data_corr.loc['revenue','budget'])

Correlation Between Revenue And Budget :  0.6885561524636727


#how does vote average(ratings) change according to the release year.
#make a scatter plot using 'regplot' between 'year' and 'vote average'.
ax = sns.regplot(x=reader['release_year'], y=reader['vote_average'],color='c')

#setup the title and the labels of the scatter plot.
ax.set_title("Release Year Vs Vote Average",fontsize=13)
ax.set_xlabel("Release Year",fontsize=12)
ax.set_ylabel("Vote Average",fontsize=12)

#set the figure size and print the correlation between them.
sns.set(rc={'figure.figsize':(6,4)})
sns.set_style("whitegrid")
print("Correlation Between Release Year And Vote Average : ",data_corr.loc['release_year','vote_average'])

Correlation Between Release Year And Vote Average :  -0.11757558609467468


#how does vote average(ratings) change according to the release year.
#make a scatter plot using 'regplot' between 'year' and 'vote average'.
ax = sns.regplot(x=reader['popularity'],y=reader['Profit'],color='c')

#setup the title and the labels of the scatter plot.
ax.set_title("Popularity Vs Profit",fontsize=13)
ax.set_xlabel("Popularity",fontsize=12)
ax.set_ylabel("Profit",fontsize=12)

#setup the figure size.
sns.set(rc={'figure.figsize':(6,4)})
sns.set_style("whitegrid")

#find the correlation between them.
print("Correlation Between Popularity And Profit : ",data_corr.loc['popularity','Profit'])

Correlation Between Popularity And Profit :  0.6159162443059019


#extract the month number from the release date.
month_release = reader['release_date'].dt.month

#count the movies in each month using value_counts().
number_of_release = month_release.value_counts().sort_index()
months=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
number_of_release = pd.DataFrame(number_of_release)
number_of_release['month'] = months

#change the column name of the new dataframe 'number_of_release'
number_of_release.rename(columns = {'release_date':'number_of_release'},inplace=True)

#plot the bar graph using plot.
number_of_release.plot(x='month',kind='bar',fontsize = 11,figsize=(8,6))

#set the labels and titles of the plot.
plt.title('Months vs Number Of Movie Releases',fontsize = 15)
plt.xlabel('Month',fontsize = 13)
plt.ylabel('Number of movie releases',fontsize = 13)
sns.set_style("darkgrid")


#which month made the highest average revenue?.
#make a dataframe with in which store the release month of each movie.
month_release = pd.DataFrame(month_release)

#change the column name of the new dataframe 'month_release'.
month_release.rename(columns = {'release_date':'release_month'},inplace=True)

#add a new column 'revenue' in the dataframe 'month_release'.
month_release['revenue'] = reader['revenue']

#make the group of the data according to their month and calculate the mean revenue of each month.
mean_revenue  = month_release.groupby('release_month').mean()
mean_revenue['month'] = months

#make the bar plot using pandas plot function.
mean_revenue.plot(x='month',kind='bar',figsize = (8,6),fontsize=11)

#setup the title and lables of the plot.
plt.title('Average revenue by month (1960 - 2015)',fontsize = 15)
plt.xlabel('Month',fontsize = 13)
plt.ylabel('Average Revenue',fontsize = 13)
sns.set_style("darkgrid")


#make a function will will split the string and return the count of each genre.
def count_genre(x):
    #concatenate all the rows of the genrs.
    data_plot = reader[x].str.cat(sep = '|')
    data = pd.Series(data_plot.split('|'))
    #conts each of the genre and return.
    info = data.value_counts(ascending=False)
    return info

#call the function for counting the movies of each genre.
total_genre_movies = count_genre('genres')
#plot a 'barh' plot using plot function for 'genre vs number of movies'.
total_genre_movies.plot(kind= 'barh',figsize = (13,6),fontsize=12,colormap='tab20c')

#setup the title and the labels of the plot.
plt.title("Genre With Highest Release",fontsize=15)
plt.xlabel('Number Of Movies',fontsize=13)
plt.ylabel("Genres",fontsize= 13)
sns.set_style("whitegrid")


i = 0
genre_count = []
for genre in total_genre_movies.index:
    genre_count.append([genre, total_genre_movies[i]])
    i = i+1
    
plt.rc('font', weight='bold')
f, ax = plt.subplots(figsize=(5, 5))
genre_count.sort(key = lambda x:x[1], reverse = True)
labels, sizes = zip(*genre_count)
labels_selected = [n if v > sum(sizes) * 0.01 else '' for n, v in genre_count]
ax.pie(sizes, labels=labels_selected,
       autopct = lambda x:'{:2.0f}%'.format(x) if x > 1 else '',
       shadow=False, startangle=0)
ax.axis('equal')
plt.tight_layout()


#map all the rows of genres in a list.
genre_details = list(map(str,(reader['genres'])))
genre = ['Adventure', 'Science Fiction', 'Fantasy', 'Crime', 'Western', 'Family','nan', 'Animation','War','Mystery','Romance','TV Movie','Action', 'Thriller','Comedy','Drama' , 'History', 'Music', 'Horror', 'Documentary', 'Foreign']

#make the numpy array of year and popularity which contain all the rows of release_year and popularity column. 
year = np.array(reader['release_year'])
popularity = np.array(reader['popularity'])

#make a null dataframe which indexs are genres and columns are years.
popularity_df = pd.DataFrame(index = genre, columns = range(1960, 2016))
#change all the values of the dataframe from NAN to zero.
popularity_df = popularity_df.fillna(value = 0.0)

z = 0
for i in genre_details:
    split_genre = list(map(str,i.split('|')))
    popularity_df.loc[split_genre, year[z]] = popularity_df.loc[split_genre, year[z]] + popularity[z]
    z+=1


#calculate the standard deviation for the accurate results.
def calculate_std(x):
    return (x-x.mean())/x.std(ddof=0)

popular_genre = calculate_std(popularity_df)
popular_genre.head()


#plot the barh plot of the standardised data.
popular_genre.iloc[10:,53:].plot(kind='barh',figsize = (15,6),fontsize=13)

#setup the title and labels of the plot.
plt.title("Most Popular Genre Over Year To Year",fontsize=15)
plt.xlabel("Popularity (Standerd Units)",fontsize=14)
plt.ylabel("Genres",fontsize = 14)
sns.set_style("whitegrid")


#How the popularity of the genre differ year by year.
sns.set_style("whitegrid")
#make a subplot of size 3,3.
fig, ax = plt.subplots(3,3,figsize = (16,10))

#set the title of the subplot.
fig.suptitle('Genre Popularity Over Year To Year',fontsize = 16)

#plot the 'Drama' genre plot see the popularity difference over year to year.
popular_genre.loc['Drama'].plot(label = "Drama",color = '#f67280',ax = ax[0][0],legend=True)

#plot the 'Action' genre plot see the popularity difference over year to year.
popular_genre.loc['Action'].plot(label = "Horror",color='#33FFB5',ax = ax[0][1],legend=True)

#plot the 'Comedy' genre plot see the popularity difference over year to year.
popular_genre.loc['Comedy'].plot(label = "Comedy",color='#fe5f55',ax = ax[0][2],legend=True)

#accordig to the previous plot the 'Thriller','Science Fiction', 'Romance', 'Music','Adventure' and 'Crime'.
popular_genre.loc['Thriller'].plot(label = "Horror",color='#00818a',ax = ax[1][0],legend=True)
popular_genre.loc['Science Fiction'].plot(label = "Science Fiction",color='#6f6600',ax = ax[1][1],legend=True)
popular_genre.loc['Romance'].plot(label = "Romance",color='#1a2c5b',ax = ax[1][2],legend=True)
popular_genre.loc['Music'].plot(label = "Music",color='#db3b61',ax = ax[2][0],legend=True)
popular_genre.loc['Adventure'].plot(label = "Adventure",color='#08c299',ax = ax[2][1],legend=True)
popular_genre.loc['Crime'].plot(label = "Crime",color='c',ax = ax[2][2],legend=True)

<AxesSubplot:>


#call the 'count_genre' function which we made earlier for spiliing and counting genres.
#it wiil count number of movies done by each actor.
count_actor_flims = count_genre('cast')

#plot the barh plot.
count_actor_flims.iloc[:20].plot.bar(figsize=(13,6),colormap= 'tab20c',fontsize=12)

#setup the title and the labels of the plot.
plt.title("Most Frequent Actor",fontsize=15)
plt.xticks(rotation = 70)
plt.xlabel('Actor',fontsize=13)
plt.ylabel("Number Of Movies",fontsize= 13)
sns.set_style("whitegrid")


#call the 'count_genre' function which we made earlier for spiliing and counting genres.
#it wiil count number of movies released by each production company.
production_companies = count_genre('production_companies')

#plot he barh plot.
production_companies.iloc[:20].plot(kind='barh',figsize=(16,8),fontsize=13)
plt.title("Production Companies Vs Number Of Movies",fontsize=15)
plt.xlabel('Number Of Movies',fontsize=14)
sns.set_style("whitegrid")


##map all the rows of production_companies in a list.
data1 = list(map(str,(reader['production_companies'])))
production_companies = pd.DataFrame(production_companies)

#make the list of profit and production_companies which contain all the rows of profit and production_companies column. 
data3 = list(production_companies.index.values)
#append the nan in the data3 because production_companies row has null values also.
data3.append('nan')
reader['Profit'] = reader['Profit'].replace(np.NAN,0)
profit = np.array(reader['Profit'])

#make a null dataframe which indexs are genres and has only one column Total Profit.
companies_df = pd.DataFrame(index = data3 ,columns =np.arange(0,1,1) )
#change all the values of the dataframe from NAN to zero.
companies_df = companies_df.fillna(value = 0.0)

z = 0
for i in data1:
    split_companies = list(map(str,i.split('|')))
    companies_df.loc[split_companies] = companies_df.loc[split_companies] + profit[z]
    z+=1


#Rename the column name 
companies_df = companies_df.rename(columns={0:'Total Profit'})
#plot the barh plot
companies_df[:20].plot.barh(figsize=(13,6),fontsize=12,legend=True)
plt.title("Production Companies Vs Life Time Profit",fontsize=15)
plt.xlabel('Total Profit',fontsize=13)
sns.set_style("whitegrid")


#call the 'count_genre' function which we made earlier for spiliing and counting genres.
#it wiil count number of movies directed by each diretor.
count_director_movies = count_genre('director')

#plot a barh graph
count_director_movies.iloc[:20].plot(kind='bar',figsize=(13,6),fontsize=12)

#setup the title and the labels 
plt.title("Director Vs Number Of Movies",fontsize=15)
plt.xticks(rotation=70)
plt.ylabel("Number Of Movies",fontsize= 13)
sns.set_style("whitegrid")


info = pd.DataFrame(reader['revenue'].sort_values(ascending=False))
data_set = ['id','popularity','budget','original_title','cast','director','runtime','genres','vote_average','release_year']
for i in data_set:
    info[i] = reader[i]
info.head(3)


fig, axes = plt.subplots(2,2,figsize = (16,6))
fig.suptitle("Revenue Vs (Budget,Popularity,Vote Average,Runtime)",fontsize=14)
#info.plot(x = ['revenue'],y = ['budget'],kind = "scatter",ax = axes[0][0])
sns.regplot(x=reader['revenue'], y=reader['budget'],color='c',ax=axes[0][0])
sns.regplot(x=reader['revenue'], y=reader['popularity'],color='c',ax=axes[0][1])
sns.regplot(x=reader['revenue'], y=reader['vote_average'],color='c',ax=axes[1][0])
sns.regplot(x=reader['revenue'], y=reader['runtime'],color='c',ax=axes[1][1])
#axes.scatter(info['revenue'],info['vote_average'])
sns.set_style("whitegrid")


def plot_correlation_map( df ):
    corr = df.corr()
    _ , ax = plt.subplots( figsize =( 12 , 10 ) )
    cmap = sns.diverging_palette( 240 , 10 , as_cmap = True )
    _ = sns.heatmap(corr,cmap = cmap,square=True, cbar_kws={ 'shrink' : .9 }, ax=ax, annot = True, annot_kws = { 'fontsize' : 12 })


plot_correlation_map(reader[['popularity','budget','revenue','runtime','vote_count','vote_average']])

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	Twenty-two years after the events of Jurassic ...	124	Action\|Adventure\|Science Fiction\|Thriller	Universal Studios\|Amblin Entertainment\|Legenda...	6/9/15	5562	6.5	2015	1.379999e+08	1.392446e+09
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy\|Charlize Theron\|Hugh Keays-Byrne\|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	...	An apocalyptic story set in the furthest reach...	120	Action\|Adventure\|Science Fiction\|Thriller	Village Roadshow Pictures\|Kennedy Miller Produ...	5/13/15	6185	7.1	2015	1.379999e+08	3.481613e+08

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
10864	21449	tt0061177	0.064317	0	0	What's Up, Tiger Lily?	Tatsuya Mihashi\|Akiko Wakabayashi\|Mie Hama\|Joh...	NaN	Woody Allen	WOODY ALLEN STRIKES BACK!	...	In comic Woody Allen's film debut, he took the...	80	Action\|Comedy	Benedict Pictures Corp.	11/2/66	22	5.4	1966	0.000000	0.0
10865	22293	tt0060666	0.035919	19000	0	Manos: The Hands of Fate	Harold P. Warren\|Tom Neyman\|John Reynolds\|Dian...	NaN	Harold P. Warren	It's Shocking! It's Beyond Your Imagination!	...	A family gets lost on the road and stumbles up...	74	Horror	Norm-Iris	11/15/66	15	1.5	1966	127642.279154	0.0

	id	popularity	budget	revenue	runtime	vote_count	vote_average	release_year	budget_adj	revenue_adj
count	10866.000000	10866.000000	1.086600e+04	1.086600e+04	10866.000000	10866.000000	10866.000000	10866.000000	1.086600e+04	1.086600e+04
mean	66064.177434	0.646441	1.462570e+07	3.982332e+07	102.070863	217.389748	5.974922	2001.322658	1.755104e+07	5.136436e+07
std	92130.136561	1.000185	3.091321e+07	1.170035e+08	31.381405	575.619058	0.935142	12.812941	3.430616e+07	1.446325e+08
min	5.000000	0.000065	0.000000e+00	0.000000e+00	0.000000	10.000000	1.500000	1960.000000	0.000000e+00	0.000000e+00
25%	10596.250000	0.207583	0.000000e+00	0.000000e+00	90.000000	17.000000	5.400000	1995.000000	0.000000e+00	0.000000e+00
50%	20669.000000	0.383856	0.000000e+00	0.000000e+00	99.000000	38.000000	6.000000	2006.000000	0.000000e+00	0.000000e+00
75%	75610.000000	0.713817	1.500000e+07	2.400000e+07	111.000000	145.750000	6.600000	2011.000000	2.085325e+07	3.369710e+07
max	417859.000000	32.985763	4.250000e+08	2.781506e+09	900.000000	9767.000000	9.200000	2015.000000	4.250000e+08	2.827124e+09

	1386	2244
id	19995	46528
popularity	9.432768	0.25054
budget	237000000	425000000
revenue	2781505847	11087569
original_title	Avatar	The Warrior's Way
cast	Sam Worthington\|Zoe Saldana\|Sigourney Weaver\|S...	Kate Bosworth\|Jang Dong-gun\|Geoffrey Rush\|Dann...
director	James Cameron	Sngmoo Lee
keywords	culture clash\|future\|space war\|space colony\|so...	assassin\|small town\|revenge\|deception\|super speed
runtime	162	100
genres	Action\|Adventure\|Fantasy\|Science Fiction	Adventure\|Fantasy\|Action\|Western\|Thriller
production_companies	Ingenious Film Partners\|Twentieth Century Fox ...	Boram Entertainment Inc.
release_date	2009-12-10 00:00:00	2010-12-02 00:00:00
vote_count	8458	74
vote_average	7.1	6.4
release_year	2009	2010
Profit	2544505847	-413912431

	2244	1151
id	46528	287524
popularity	0.25054	0.177102
budget	425000000.0	1.0
revenue	11087569	0
original_title	The Warrior's Way	Fear Clinic
cast	Kate Bosworth\|Jang Dong-gun\|Geoffrey Rush\|Dann...	Thomas Dekker\|Robert Englund\|Cleopatra Coleman...
director	Sngmoo Lee	Robert Hall
keywords	assassin\|small town\|revenge\|deception\|super speed	phobia\|doctor\|fear
runtime	100	95
genres	Adventure\|Fantasy\|Action\|Western\|Thriller	Horror
production_companies	Boram Entertainment Inc.	Dry County Films\|Anchor Bay Entertainment\|Movi...
release_date	2010-12-02 00:00:00	2014-10-31 00:00:00
vote_count	74	15
vote_average	6.4	4.1
release_year	2010	2014
Profit	-413912431	-1

Investigate a Dataset (TMDb Movie Data)¶

Table of Contents¶

Introduction¶

Dataset¶

Questions¶

Data Wrangling¶

General Properties¶

Data Cleaning (Removing The Unused Information From The Dataset)¶

Information That We Need To Delete Or Modify¶

Since now we have the columns, rows and format of the dataset in right way, its time to investigate the data for the questions asked.¶

Exploratory Data Analysis¶

Research Question 1 : Which year has the highest release of movies?¶

After Seeing the plot and the output we can conclude that year 2014 year has the highest release of movies (700) followed by year 2013 (659) and year 2015 (629).¶

Research Question 2 : Which Movie Has The Highest Or Lowest Profit?¶

Research Question 3 : Movie with Highest And Lowest Budget?¶

Research Question 4 : Movie with Largest And Lowest Earned Revenue?¶

Research Question 5 : Movie with Longest And Shortest Rintime?¶

Research Question 6 : Movie with Highest And Lowest Votes?¶

Research Question 7 : Which Year Has The Highest Profit Rate?¶

Research Question 8 : Which length movies most liked by the audiences according to their popularity?¶

Research Question 9: Average Runtime Of Movies From Year To Year?¶

Research Question 10: How Does The Revenue And Popularity differs Budget And Runtime? And How Does Popularity Depends On Profit?¶

Research Question 11: Which Month Released Highest Number Of Movies In All Of The Years? And Which Month Made The Highest Average Revenue?¶

Research Question 12: Which Genre Has The Highest Release Of Movies?¶

Research Question 13: Which genres are most popular from year to year?¶

Reasearch Question 14: Most Frequent Actor?¶

Reasearch Question 15: Top 20 Production Companies With Higher Number Of Release?¶

Reasearch Question 16: Life Time Profit Earn By Each Production Company¶

Research Question 17 : Top 20 Director Who Directs Maximum Movies?¶

Research Question 18: What Kind Of Properties Are Associated With Movies With High Revenue?¶

Conclusions¶

	1386	48
id	19995	265208
popularity	9.432768	2.93234
budget	237000000.0	30000000.0
revenue	2781505847	0
original_title	Avatar	Wild Card
cast	Sam Worthington\|Zoe Saldana\|Sigourney Weaver\|S...	Jason Statham\|Michael Angarano\|Milo Ventimigli...
director	James Cameron	Simon West
keywords	culture clash\|future\|space war\|space colony\|so...	gambling\|bodyguard\|remake
runtime	162	92
genres	Action\|Adventure\|Fantasy\|Science Fiction	Thriller\|Crime\|Drama
production_companies	Ingenious Film Partners\|Twentieth Century Fox ...	Current Entertainment\|Lionsgate\|Sierra / Affin...
release_date	2009-12-10 00:00:00	2015-01-14 00:00:00
vote_count	8458	481
vote_average	7.1	5.3
release_year	2009	2015
Profit	2544505847	-30000000

	3894	4883
id	125336	142563
popularity	0.006925	0.078472
budget	NaN	NaN
revenue	0	0
original_title	The Story of Film: An Odyssey	Fresh Guacamole
cast	Mark Cousins\|Jean-Michel Frodon\|Cari Beauchamp...	NaN
director	Mark Cousins	PES
keywords	cinema\|nouvelle vague\|hindi cinema\|cinema novo...	NaN
runtime	900.0	2.0
genres	Documentary	Animation
production_companies	NaN	NaN
release_date	2011-09-03 00:00:00	2012-03-02 00:00:00
vote_count	14	29
vote_average	9.2	7.9
release_year	2011	2012
Profit	0	0

	3894	7772
id	125336	25055
popularity	0.006925	0.12112
budget	NaN	NaN
revenue	0	0
original_title	The Story of Film: An Odyssey	Transmorphers
cast	Mark Cousins\|Jean-Michel Frodon\|Cari Beauchamp...	Matthew Wolf\|Amy Weber\|Shaley Scott\|Eliza Swen...
director	Mark Cousins	Leigh Scott
keywords	cinema\|nouvelle vague\|hindi cinema\|cinema novo...	futuristic
runtime	900.0	86.0
genres	Documentary	Action\|Adventure\|Science Fiction
production_companies	NaN	Asylum, The
release_date	2011-09-03 00:00:00	2007-06-26 00:00:00
vote_count	14	10
vote_average	9.2	1.5
release_year	2011	2007
Profit	0	0

	1960	1961	1962	1963	1964	1965	1966	1967	1968	1969	...	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
Adventure	0.753047	1.338265	1.996432	1.398764	0.550490	1.124313	0.951448	1.817532	1.534066	1.097634	...	0.608896	0.520337	0.755137	0.977663	1.034638	0.586530	0.732253	0.755530	1.174435	1.614759
Science Fiction	-0.467064	-0.361952	-0.640003	-0.646572	-0.667888	-0.869288	0.097082	-0.634089	1.452081	-0.710447	...	-0.285369	-0.305635	0.082456	0.679288	-0.104266	0.212188	0.189865	0.575138	0.902119	1.231957
Fantasy	-0.528631	-0.744050	-0.691929	-0.895883	-0.012691	-0.992961	-0.585750	-0.706100	-0.055098	-0.771756	...	0.064256	0.322410	0.008741	0.178378	0.153510	0.028812	0.081027	-0.182739	-0.108344	-0.320493
Crime	-0.607773	-0.111525	-0.192078	-0.020849	0.082887	-0.523188	-0.034184	0.517043	0.209143	0.593035	...	0.085715	0.302191	0.298300	-0.395319	-0.171136	-0.059809	0.092316	0.347388	-0.219402	0.027262
Western	0.705451	-0.588007	-0.266409	-0.863575	-1.009777	-0.527569	-0.028313	-0.775331	-0.824684	0.996730	...	-1.032584	-1.024569	-1.058297	-1.111565	-0.974311	-0.992158	-0.868553	-0.974491	-0.925887	-0.759302

	revenue	id	popularity	budget	original_title	cast	director	runtime	genres	vote_average	release_year
1386	2.781506e+09	19995	9.432768	237000000.0	Avatar	Sam Worthington\|Zoe Saldana\|Sigourney Weaver\|S...	James Cameron	162.0	Action\|Adventure\|Fantasy\|Science Fiction	7.1	2009
3	2.068178e+09	140607	11.173104	200000000.0	Star Wars: The Force Awakens	Harrison Ford\|Mark Hamill\|Carrie Fisher\|Adam D...	J.J. Abrams	136.0	Action\|Adventure\|Science Fiction\|Fantasy	7.5	2015
5231	1.845034e+09	597	4.355219	200000000.0	Titanic	Kate Winslet\|Leonardo DiCaprio\|Frances Fisher\|...	James Cameron	194.0	Drama\|Romance\|Thriller	7.3	1997